Photovoltaic (PV) systems in a power grid are expected to exhibit predictable power generation behaviors. Predicable measured and forecasted power production are particularly crucial when a photovoltaic fleet makes a significant contribution to a power grid. Power production forecasting involves first obtaining a prediction of solar irradiance, as derived from ground-based measurements, satellite imagery, numerical weather prediction models, or other sources. The predicted solar irradiance and each photovoltaic plant's system configuration specification is then used in a photovoltaic simulation model to generate a power production forecast. Individual forecasts can be combined into a fleet forecast, such as described in commonly-assigned U.S. Pat. Nos. 8,165,811; 8,165,812; 8,165,813, all issued to Hoff on Apr. 24, 2012; U.S. Pat. Nos. 8,326,535; 8,326,536, issued to Hoff on Dec. 4, 2012; and U.S. Pat. No. 8,335,649, issued to Hoff on Dec. 18, 2012, the disclosures of which are incorporated by reference.
A single photovoltaic system's power capacity, expressed in units of Watt peak (Wp), is measured by maximum power output as determined under standard test conditions. Actual power can vary from the rated system power capacity depending on geographic location, time of day, weather conditions, and other factors. Moreover, photovoltaic fleets scattered over a large area are subject to different location-specific cloud conditions with a consequential effect on aggregate power output.
As a result, photovoltaic fleets operating under cloudy conditions can exhibit variable and unpredictable performance. Conventionally, fleet variability is determined by centrally collecting direct power measurements from individual systems or equivalent indirectly-derived power measurements. To be of optimal usefulness, the direct power measurement data must be collected in near-real time at fine-grained time intervals to generate high resolution power output time series data. The practicality of this approach diminishes as the number of systems, variations in system specifications, and geographic dispersion grow. Moreover, the costs and feasibility of relying upon high speed data collection and analysis becomes insurmountable due to the transmission bandwidth and storage space needed and the processing resources required to scale quantitative power measurement analysis upwards as fleet size grows.
For instance, one direct approach to obtaining high speed time series power production data from a photovoltaic fleet is to install physical meters on every system, record the electrical power output at a desired time interval, and sum the power output across all of the systems in the fleet. The totalized power data can then be used to calculate time-averaged fleet power and variance, and similar values for the rate of change of fleet power. An equivalent direct approach for a future photovoltaic fleet or an existing fleet with incomplete metering and telemetry is to collect solar irradiance data from a dense network of weather monitoring stations covering all anticipated locations at a desired time interval, use a photovoltaic performance model to simulate the time series output data for each system, and sum the results at each time interval.
Several difficulties arise with both approaches to obtaining high speed time series power production data. First, in terms of physical plant, calibrating, installing, operating, and maintaining meters and weather stations is expensive and detracts from cost savings otherwise afforded through a renewable energy source. Similarly, collecting, validating, transmitting, and storing high speed data for every photovoltaic system or location requires expensive data communications and processing infrastructure at significant expense. Moreover, data loss occurs whenever instrumentation or data communications fail. Second, in terms of inherent limitations, both direct approaches only work for times, locations, and photovoltaic system configurations when and where meters are pre-installed. Both direct approaches also cannot be used to directly forecast future system performance since meters must be physically present at the time and location of interest. Also, data must be recorded at the time resolution that corresponds to the desired output time resolution. While low time-resolution results can be calculated from high resolution data, the opposite calculation is not possible. For example, photovoltaic fleet behavior with a 10-second resolution cannot be determined from data collected with a 15-minute resolution.
The few solar data networks that exist in the United States, such as the ARM network, described in G. M. Stokes et al., “The Atmospheric Radiation Measurement (ARM) Program: Programmatic Background and Design of the Cloud and Radiation Test Bed,” Bulletin of Am. Meteor. Soc., Vol. 75, pp. 1201-1221 (1994), the disclosure of which is incorporated by reference, and the SURFRAD network, do not have high density networks (the closest pair of stations in the ARM network are 50 km apart) nor have they been collecting data at a fast rate (the fastest rate is 20 seconds in the ARM network and one minute in the SURFRAD network). The limitations of the direct measurement approaches have prompted researchers to evaluate other alternatives. Researchers have installed dense networks of solar monitoring devices in a few limited locations, such as described in S. Kuszamaul et al., “Lanai High-Density Irradiance Sensor Network for Characterizing Solar Resource Variability of MW-Scale PV System.” 35th Photovoltaic Specialists Conf., Honolulu, Hi. (Jun. 20-25, 2010), and R. George, “Estimating Ramp Rates for Large PV Systems Using a Dense Array of Measured Solar Radiation Data,” Am. Solar Energy Society Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. As data are being collected, the researchers examine the data to determine if there are underlying models that can translate results from these devices to photovoltaic fleet production at a much broader area, yet fail to provide translation of the data. In addition, half-hour or hourly satellite irradiance data for specific locations and time periods of interest have been combined with randomly selected high speed data from a limited number of ground-based weather stations, such as described in CAISO 2011. “Summary of Preliminary Results of 33% Renewable Integration Study—2010,” Cal. Public Util. Comm. LTPP No. R.10-05-006 (Apr. 29, 2011) and J. Stein, “Simulation of 1-Minute Power Output from Utility-Scale Photovoltaic Generation Systems,” Am. Solar Energy Society Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. This approach, however, does not produce time synchronized photovoltaic fleet variability for any particular time period because the locations of the ground-based weather stations differ from the actual locations of the fleet. While such results may be useful as input data to photovoltaic simulation models for purpose of performing high penetration photovoltaic studies, they are not designed to produce data that could be used in grid operational tools.
Accurate photovoltaic system configuration specifications are as important to photovoltaic power output forecasting as obtaining reliable solar irradiance forecasts. A system specification typically includes geographic location, photovoltaic and inverter ratings, tilt and azimuth angles, other losses, obstruction profile (elevation angles in multiple azimuth directions), plus other information and factors relevant to the system. When available, user-supplied system specifications have the advantage of simplicity; however, system specifications provided by an owner or operator can vary in terms of completeness, quality, and correctness, which in turn skews power output forecasting. Moreover, in some situations, system specifications may simply not be available, as can happen with privately-owned systems. Residential systems, for example, are typically not controlled or accessible by power grid operators and other personnel who need to understand and gauge expected photovoltaic power output capabilities and shortcomings, and even large utility-connected systems may have specifications that are not publicly available due to privacy or security reasons. In the alternative, system specifications can be indirectly inferred from measured photovoltaic production data, although inferring system specifications can be daunting, particularly from a computational load perspective if the number of possible combinations of system specification parameters are not properly bounded.
Although an accurate system specification provides a starting point for power output forecasting, annual photovoltaic power production can and often does vary for a variety of reasons independent of photovoltaic configuration. First, differences in weather conditions can cause year-to-year power production variation. Second, utility power outages can result in lost production. Third, data collection problems can result in data loss, and reported production fails to match actual production. Fourth, differences in soiling of photovoltaic panels can cause production to vary. Finally, photovoltaic system degradation, which happens gradually over time, can reduce production. Of these factors, degradation carries the potential for the highest negative financial consequences over the long-term because degradation impacts power output and is both cumulative and permanent, whereas transient conditions, such as inclement weather or a utility power outage, only sporadically affect power production.
Due to such concerns, photovoltaic system manufacturers and third party companies provide warranties and performance guarantees to protect owners and operators of photovoltaic systems against degradation. Warranties in the U.S. range from 10-year photovoltaic panel warranties to 25-year complete photovoltaic system warranties, such as offered by SunPower Corporation, San Jose, Calif. For instance, SunPower's Complete Confidence Warranty covers all repair or replacement costs if the warranted system declines in power output more than eight percent over a 25-year period, which translates to a maximum system degradation of 0.34 percent per year.
While both accurate power forecasting and warranty considerations require a way to effectively gauge degradation, detecting photovoltaic system degradation in the early years of a system's life is challenging because the effect of degradation in any given year is likely to be small compared to year-to-year weather variability. Moreover, directly measuring photovoltaic system degradation is costly and inexact. Consider two options for measuring degradation, examining the change in historical photovoltaic energy production over time, and comparing instantaneous power measurements at two different points in time.
Comparing historical photovoltaic energy production over time is challenging. First, year-to-year weather variability can cause more change in photovoltaic production between two years than the results of expected degradation. FIG. 1 is a graph showing, by way of example, the year-to-year variability of global horizontal irradiance (GHI) over the 19-year period in Napa, Calif. The x-axis indicates year. The y-axis represents the ratio of annual to average irradiance. The graph indicates that irradiance varied by ten percent (+/−five percent) in this location, which indicates that GHI year-to-year variability would exceed the limits on warranted degradation, such as provided by SunPower's Complete Confidence Warranty. Second, power outages can reduce actual production in some years. Third, data collection system issues can result in an incomplete data set, so that reported production does not match actual production. These last two reasons prevent a year-to-year comparison of production outputs due to the uncertainty over whether each year's data set is complete. Thus, directly comparing measured energy is an unsatisfactory solution.
Comparing power measurements at different points in time is both challenging. First, directly measuring degradation using on-site tests is costly and the test must be performed under identical weather conditions, including irradiance, ambient temperature, and wind speed, and shading conditions to make the power readings comparable. Second, the photovoltaic system must be in the same condition for each on-site test, which requires that the system be thoroughly cleaned prior to the time of each power reading measurement. Third, on-site tests require personnel to be on-site, which poses scheduling concerns and increases costs. Thus, directly comparing measured power is an unsatisfactory solution.
Therefore, a need remains for a cost-effective and practicable approach to forecasting long-term photovoltaic power generation system degradation.